diff --git a/bn_persona_model/persona_model_test.bif b/bn_persona_model/persona_model_test.bif
index 28727a89c3dae174cb90be06ea3cc7b102dd5160..f3c919a6f240b9da18501e282a7c1dd09a59438a 100644
--- a/bn_persona_model/persona_model_test.bif
+++ b/bn_persona_model/persona_model_test.bif
@@ -6,39 +6,36 @@ network persona_model {
variable agent_assistance {
type discrete [ 6 ] { lev_0, lev_1, lev_2, lev_3, lev_4, lev_5 };
}
-variable attempt_t0 {
+variable attempt {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
}
-variable game_state_t0 {
- type discrete [ 3 ] { beg, mid, end };
-}
-variable attempt_t1 {
- type discrete [ 1 ] { att_1, att_2, att_3, att_4};
-}
-variable game_state_t1 {
+variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
+
variable user_action {
type discrete [ 3 ] { correct, wrong, timeout };
}
variable agent_feedback {
type discrete [ 2 ] { no, yes };
}
+variable user_memory {
+ type discrete [ 3 ] {low, medium, high};
+}
+
+variable user_reactivity {
+ type discrete [ 3 ] {low, medium, high};
+}
%INDIVIDUAL PROBABILITIES DEFINITION
+
probability ( agent_assistance ) {
table 0.17, 0.16, 0.16, 0.17, 0.17, 0.17;
}
-probability ( game_state_t0 ) {
+probability ( game_state) {
table 0.34, 0.33, 0.33;
}
-probability ( attempt_t0 ) {
- table 0.25, 0.25, 0.25, 0.25;
-}
-probability ( game_state_t1 ) {
- table 0.34, 0.33, 0.33;
-}
-probability ( attempt_t1 ) {
+probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
probability ( user_action ) {
@@ -47,98 +44,65 @@ probability ( user_action ) {
probability ( agent_feedback ) {
table 0.5, 0.5;
}
-probability(agent_assistance | agent_feedback) {
- (yes) 0.4, 0.3, 0.2, 0.1, 0.0, 0.0
- (no) 0.0, 0.0, 0.1, 0.2, 0.3, 0.4
-}
-
-probability (user_action | game_state_t0, attempt_t0, agent_assistance){
- (beg, att_1, lev_0) 0.1, 0.9, 0.0;
- (beg, att_2, lev_0) 0.2, 0.8, 0.0;
- (beg, att_3, lev_0) 0.3, 0.7, 0.0;
- (beg, att_4, lev_0) 0.4, 0.6, 0.0;
- (beg, att_1, lev_1) 0.2, 0.8, 0.0;
- (beg, att_2, lev_1) 0.3, 0.7, 0.0;
- (beg, att_3, lev_1) 0.4, 0.6, 0.0;
- (beg, att_4, lev_1) 0.5, 0.5, 0.0;
- (beg, att_1, lev_2) 0.3, 0.7, 0.0;
- (beg, att_2, lev_2) 0.4, 0.6, 0.0;
- (beg, att_3, lev_2) 0.5, 0.5, 0.0;
- (beg, att_4, lev_2) 0.6, 0.4, 0.0;
- (beg, att_1, lev_3) 0.4, 0.6, 0.0;
- (beg, att_2, lev_3) 0.5, 0.5, 0.0;
- (beg, att_3, lev_3) 0.6, 0.4, 0.0;
- (beg, att_4, lev_3) 0.7, 0.3, 0.0;
- (beg, att_1, lev_4) 1.0, 0.0, 0.0;
- (beg, att_2, lev_4) 1.0, 0.0, 0.0;
- (beg, att_3, lev_4) 1.0, 0.0, 0.0;
- (beg, att_4, lev_4) 1.0, 0.0, 0.0;
- (beg, att_1, lev_5) 1.0, 0.0, 0.0;
- (beg, att_2, lev_5) 1.0, 0.0, 0.0;
- (beg, att_3, lev_5) 1.0, 0.0, 0.0;
- (beg, att_4, lev_5) 1.0, 0.0, 0.0;
-
- (mid, att_1, lev_0) 0.1, 0.9, 0.0;
- (mid, att_2, lev_0) 0.2, 0.8, 0.0;
- (mid, att_3, lev_0) 0.3, 0.7, 0.0;
- (mid, att_4, lev_0) 0.4, 0.6, 0.0;
- (mid, att_1, lev_1) 0.2, 0.8, 0.0;
- (mid, att_2, lev_1) 0.3, 0.7, 0.0;
- (mid, att_3, lev_1) 0.4, 0.6, 0.0;
- (mid, att_4, lev_1) 0.5, 0.5, 0.0;
- (mid, att_1, lev_2) 0.3, 0.7, 0.0;
- (mid, att_2, lev_2) 0.4, 0.6, 0.0;
- (mid, att_3, lev_2) 0.5, 0.5, 0.0;
- (mid, att_4, lev_2) 0.6, 0.4, 0.0;
- (mid, att_1, lev_3) 0.4, 0.6, 0.0;
- (mid, att_2, lev_3) 0.5, 0.5, 0.0;
- (mid, att_3, lev_3) 0.6, 0.4, 0.0;
- (mid, att_4, lev_3) 0.7, 0.3, 0.0;
- (mid, att_1, lev_4) 1.0, 0.0, 0.0;
- (mid, att_2, lev_4) 1.0, 0.0, 0.0;
- (mid, att_3, lev_4) 1.0, 0.0, 0.0;
- (mid, att_4, lev_4) 1.0, 0.0, 0.0;
- (mid, att_1, lev_5) 1.0, 0.0, 0.0;
- (mid, att_2, lev_5) 1.0, 0.0, 0.0;
- (mid, att_3, lev_5) 1.0, 0.0, 0.0;
- (mid, att_4, lev_5) 1.0, 0.0, 0.0;
-
- (end, att_1, lev_0) 0.1, 0.9, 0.0;
- (end, att_2, lev_0) 0.2, 0.8, 0.0;
- (end, att_3, lev_0) 0.3, 0.7, 0.0;
- (end, att_4, lev_0) 0.4, 0.6, 0.0;
- (end, att_1, lev_1) 0.2, 0.8, 0.0;
- (end, att_2, lev_1) 0.3, 0.7, 0.0;
- (end, att_3, lev_1) 0.4, 0.6, 0.0;
- (end, att_4, lev_1) 0.5, 0.5, 0.0;
- (end, att_1, lev_2) 0.3, 0.7, 0.0;
- (end, att_2, lev_2) 0.4, 0.6, 0.0;
- (end, att_3, lev_2) 0.5, 0.5, 0.0;
- (end, att_4, lev_2) 0.6, 0.4, 0.0;
- (end, att_1, lev_3) 0.4, 0.6, 0.0;
- (end, att_2, lev_3) 0.5, 0.5, 0.0;
- (end, att_3, lev_3) 0.6, 0.4, 0.0;
- (end, att_4, lev_3) 0.7, 0.3, 0.0;
- (end, att_1, lev_4) 1.0, 0.0, 0.0;
- (end, att_2, lev_4) 1.0, 0.0, 0.0;
- (end, att_3, lev_4) 1.0, 0.0, 0.0;
- (end, att_4, lev_4) 1.0, 0.0, 0.0;
- (end, att_1, lev_5) 1.0, 0.0, 0.0;
- (end, att_2, lev_5) 1.0, 0.0, 0.0;
- (end, att_3, lev_5) 1.0, 0.0, 0.0;
- (end, att_4, lev_5) 1.0, 0.0, 0.0;
-}
-
-
-probability (game_state_t1 | user_action) {
- (correct) 0.25, 0.3, 0.45;
- (wrong) 0.33, 0.33, 0.33;
- (timeout) 0.33, 0.33, 0.33;
-}
-probability (attempt_t1 | user_action) {
- (correct) 0.1, 0.2, 0.25, 0.45;
- (wrong) 0.25, 0.25, 0.25, 0.25;
- (timeout) 0.25, 0.25, 0.25, 0.25;
+probability ( user_memory ) {
+ table 0.33, 0.34, 0.33;
+}
+probability ( user_reactivity ) {
+ table 0.33, 0.34, 0.33;
+}
+
+
+probability (game_state | user_memory){
+ (low) 0.1, 0.3, 0.6;
+ (medium) 0.2, 0.4, 0.4;
+ (high) 0.33, 0.33, 0.34;
+}
+
+probability (attempt | user_memory){
+ (low) 0.1,0.1,0.2, 0.6;
+ (medium) 0.1, 0.2, 0.3, 0.4;
+ (high) 0.25, 0.25, 0.25, 0.25;
+}
+
+probability (game_state | user_reactivity){
+ (low) 0.1, 0.3, 0.6;
+ (medium) 0.2, 0.4, 0.4;
+ (high) 0.33, 0.33, 0.34;
+}
+
+probability (attempt | user_reactivity){
+ (low) 0.1,0.1,0.2, 0.6;
+ (medium) 0.1, 0.2, 0.3, 0.4;
+ (high) 0.25, 0.25, 0.25, 0.25;
}
+
+probability (game_state | user_action) {
+ (correct) 0.30, 0.30, 0.4;
+ (wrong) 0.35, 0.35, 0.3;
+ (timeout) 0.33, 0.33, 0.34;
+}
+probability (attempt | user_action) {
+ (correct) 0.25, 0.25, 0.25, 0.25;
+ (wrong) 0.4, 0.3, 0.2, 0.1;
+ (timeout) 0.4, 0.3, 0.2, 0.1;
+}
+
+
+probability (user_action | agent_assistance, agent_feedback) {
+(lev_0, no) 0.05 0.85 0.1;
+(lev_1, no) 0.1 0.8 0.1;
+(lev_2, no) 0.2 0.7 0.1;
+(lev_3, no) 0.33 0.57 0.1;
+(lev_4, no) 0.9 0.1 0.0;
+(lev_5, no) 1.0 0.0 0.0;
+
+(lev_0, yes) 0.5 0.4 0.1;
+(lev_1, yes) 0.6 0.3 0.1;
+(lev_2, yes) 0.7 0.2 0.1;
+(lev_3, yes) 0.8 0.1 0.1;
+(lev_4, yes) 0.9 0.1 0.0;
+(lev_5, yes) 1.0 0.0 0.0;
+
+}
diff --git a/test.py b/test.py
index 66bd57cac6a41c2575e609287938cb842d8dd8b0..a2d2d907fa5a2e3bfa600c16fb579d7e9b8bf4c2 100644
--- a/test.py
+++ b/test.py
@@ -1,20 +1,97 @@
import bnlearn as bn
+import pandas as pd
-DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif')
+
+def import_data_from_csv(csv_filename, dag_filename):
+ print("/************************************************************/")
+ print("Init model")
+ DAG = bn.import_DAG(dag_filename)
+ df_caregiver = bn.sampling(DAG, n= 10000)
+ print("/************************************************************/")
+ print("real_user Model")
+ DAG_ = bn.import_DAG(dag_filename, CPD=False)
+ df_real_user = pd.read_csv(csv_filename)
+ DAG_real_user = bn.parameter_learning.fit(DAG_, df_real_user, methodtype='bayes')
+ df_real_user = bn.sampling(DAG_real_user, n=10000)
+ print("/************************************************************/")
+ print("Shared knowledge")
+ DAG_ = bn.import_DAG(dag_filename, CPD=False)
+ shared_knowledge = [df_real_user, df_caregiver]
+ conc_shared_knowledge = pd.concat(shared_knowledge)
+ DAG_shared = bn.parameter_learning.fit(DAG_, conc_shared_knowledge)
+ df_conc_shared_knowledge = bn.sampling(DAG_shared, n=10000)
+ return DAG_shared
+
+
+
+import_data_from_csv(csv_filename='bn_persona_model/cognitive_game.csv', dag_filename='bn_persona_model/persona_model_test.bif')
+# DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif')
+# G = bn.plot(DAG)
+# q1 = bn.inference.fit(DAG, variables=[ 'user_action'], evidence={
+# 'game_state': 0,
+# 'attempt':0,
+# 'agent_feedback':1,
+# 'memory': 0,
+# 'reactivity':0,
+# 'agent_assistance':0,
+#
+# })
+# df = pd.read_csv('bn_persona_model/cognitive_game.csv')
+# df = bn.sampling(DAG, n=10000)
+# #model_sl = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
+# #bn.plot(model_sl, pos=G['pos'])
+# DAG_update = bn.parameter_learning.fit(DAG, df)
+# n_game_state = 3
+# n_attempt = 4
+# n_aas = 6
+# n_af = 2
+
+# for gs in range(n_game_state):
+# for att in range(n_attempt):
+# for aas in range(n_aas):
+# for af in range(n_af):
+# q1 = bn.inference.fit(DAG_update, variables=[ 'user_action'], evidence={
+# 'game_state': gs,
+# 'attempt':att,
+# 'agent_feedback':af,
+# 'user_memory':0,
+# 'user_reactivity':0,
+# 'agent_assistance':aas})
+# print("GS:", gs, " ATT:", att, " AA", aas, " AF", af)
+#
+# df.head()
+# DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif', CPD=False)
+# bn.plot(DAG)
+# DAG_update = bn.parameter_learning.fit(DAG, df)
+# DAG_true = bn.import_DAG('bn_persona_model/persona_model_test.bif', CPD=True)
+# q1 = bn.inference.fit(DAG_update, variables=['user_action'], evidence={
+# 'game_state': 0,
+# 'attempt':2,
+# 'agent_feedback':0,
+# })
+# print("BEFORE")
+# print(q1.values)
+# df = bn.sampling(DAG_update, n=1000)
+# DAG_update = bn.parameter_learning.fit(DAG_update, df)
+# q1 = bn.inference.fit(DAG_update, variables=['user_action'], evidence={
+# 'game_state': 0,
+# 'attempt':2,
+# 'agent_feedback':0,
+# })
+# print("AFTER")
+# print(q1.values)
#df = bn.sampling(DAG, n=1000, verbose=2)
#model = bn.structure_learning.fit(df)
#G = bn.plot(model)
#DAGnew = bn.parameter_learning.fit(model, df, methodtype="bayes")
#bn.print_CPD(DAGnew)
-q1 = bn.inference.fit(DAG, variables=['user_action'], evidence={
- 'game_state_t0': 0,
- 'attempt_t0':1,
- 'game_state_t1': 0,
- 'attempt_t1':2,
- 'agent_assistance':0,
-})
-print(q1.values)
+# q1 = bn.inference.fit(DAG, variables=['user_action'], evidence={
+# 'game_state': 0,
+# 'attempt':1,
+# 'agent_feedback':1,
+# })
+# print(q1.values)
# robot_assistance = [0, 1, 2, 3, 4, 5]
# attempt_t0 = [0, 1, 2, 3]